Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations169
Missing cells586
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.6 KiB
Average record size in memory209.8 B

Variable types

Text6
DateTime1
Numeric18
Boolean4

Alerts

hidr_deriv_petr_ug_l has constant value "<0.10"Constant
colif_fecales_ufc_100ml is highly overall correlated with escher_coli_ufc_100mlHigh correlation
color is highly overall correlated with nh4_mg_lHigh correlation
dbo_mg_l is highly overall correlated with p_total_l_mg_lHigh correlation
enteroc_ufc_100ml is highly overall correlated with escher_coli_ufc_100mlHigh correlation
escher_coli_ufc_100ml is highly overall correlated with colif_fecales_ufc_100ml and 1 other fieldsHigh correlation
espumas is highly overall correlated with nh4_mg_lHigh correlation
fosf_ortofos_mg_l is highly overall correlated with nitrato_mg_l and 1 other fieldsHigh correlation
ica is highly overall correlated with oloresHigh correlation
nh4_mg_l is highly overall correlated with color and 1 other fieldsHigh correlation
nitrato_mg_l is highly overall correlated with fosf_ortofos_mg_lHigh correlation
olores is highly overall correlated with icaHigh correlation
p_total_l_mg_l is highly overall correlated with dbo_mg_l and 1 other fieldsHigh correlation
tem_agua is highly overall correlated with tem_aireHigh correlation
tem_aire is highly overall correlated with tem_aguaHigh correlation
olores is highly imbalanced (62.4%)Imbalance
color is highly imbalanced (67.4%)Imbalance
espumas is highly imbalanced (79.0%)Imbalance
tem_agua has 31 (18.3%) missing valuesMissing
tem_aire has 27 (16.0%) missing valuesMissing
od has 36 (21.3%) missing valuesMissing
ph has 44 (26.0%) missing valuesMissing
olores has 18 (10.7%) missing valuesMissing
color has 18 (10.7%) missing valuesMissing
espumas has 18 (10.7%) missing valuesMissing
mat_susp has 18 (10.7%) missing valuesMissing
colif_fecales_ufc_100ml has 25 (14.8%) missing valuesMissing
escher_coli_ufc_100ml has 25 (14.8%) missing valuesMissing
enteroc_ufc_100ml has 25 (14.8%) missing valuesMissing
nitrato_mg_l has 26 (15.4%) missing valuesMissing
nh4_mg_l has 18 (10.7%) missing valuesMissing
p_total_l_mg_l has 19 (11.2%) missing valuesMissing
fosf_ortofos_mg_l has 22 (13.0%) missing valuesMissing
dbo_mg_l has 19 (11.2%) missing valuesMissing
dqo_mg_l has 18 (10.7%) missing valuesMissing
turbiedad_ntu has 18 (10.7%) missing valuesMissing
hidr_deriv_petr_ug_l has 19 (11.2%) missing valuesMissing
cr_total_mg_l has 18 (10.7%) missing valuesMissing
cd_total_mg_l has 18 (10.7%) missing valuesMissing
clorofila_a_ug_l has 21 (12.4%) missing valuesMissing
microcistina_ug_l has 21 (12.4%) missing valuesMissing
ica has 32 (18.9%) missing valuesMissing
calidad_de_agua has 32 (18.9%) missing valuesMissing
cr_total_mg_l has 6 (3.6%) zerosZeros

Reproduction

Analysis started2024-10-11 21:41:39.205091
Analysis finished2024-10-11 21:42:43.166689
Duration1 minute and 3.96 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

sitios
Text

Distinct43
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:43.621418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length41
Median length27
Mean length23.343195
Min length8

Characters and Unicode

Total characters3945
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowCanal Villanueva y Río Luján
2nd rowCanal Villanueva y Río Luján
3rd rowCanal Villanueva y Río Luján
4th rowCanal Villanueva y Río Luján
5th rowRío Lujan y Arroyo Caraguatá
ValueCountFrequency (%)
y 40
 
5.8%
río 36
 
5.3%
de 32
 
4.7%
arroyo 24
 
3.5%
espigón 16
 
2.3%
lujan 16
 
2.3%
canal 12
 
1.8%
la 12
 
1.8%
playa 12
 
1.8%
reserva 12
 
1.8%
Other values (98) 473
69.1%
2024-10-11T18:42:44.231677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
516
 
13.1%
a 481
 
12.2%
o 316
 
8.0%
e 232
 
5.9%
r 226
 
5.7%
l 194
 
4.9%
n 192
 
4.9%
i 161
 
4.1%
s 122
 
3.1%
c 111
 
2.8%
Other values (47) 1394
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
516
 
13.1%
a 481
 
12.2%
o 316
 
8.0%
e 232
 
5.9%
r 226
 
5.7%
l 194
 
4.9%
n 192
 
4.9%
i 161
 
4.1%
s 122
 
3.1%
c 111
 
2.8%
Other values (47) 1394
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
516
 
13.1%
a 481
 
12.2%
o 316
 
8.0%
e 232
 
5.9%
r 226
 
5.7%
l 194
 
4.9%
n 192
 
4.9%
i 161
 
4.1%
s 122
 
3.1%
c 111
 
2.8%
Other values (47) 1394
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
516
 
13.1%
a 481
 
12.2%
o 316
 
8.0%
e 232
 
5.9%
r 226
 
5.7%
l 194
 
4.9%
n 192
 
4.9%
i 161
 
4.1%
s 122
 
3.1%
c 111
 
2.8%
Other values (47) 1394
35.3%

codigo
Text

Distinct44
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:44.536282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0532544
Min length5

Characters and Unicode

Total characters854
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st rowTI001
2nd rowTI001
3rd rowTI001
4th rowTI001
5th rowTI006
ValueCountFrequency (%)
ti001 4
 
2.4%
ti006 4
 
2.4%
ti002 4
 
2.4%
ti003 4
 
2.4%
ti004 4
 
2.4%
ti005 4
 
2.4%
ti007 4
 
2.4%
ti008 4
 
2.4%
ti009 4
 
2.4%
sf015 4
 
2.4%
Other values (34) 129
76.3%
2024-10-11T18:42:44.995790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 206
24.1%
I 52
 
6.1%
2 44
 
5.2%
4 40
 
4.7%
3 40
 
4.7%
S 40
 
4.7%
1 37
 
4.3%
A 37
 
4.3%
T 36
 
4.2%
5 36
 
4.2%
Other values (23) 286
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 206
24.1%
I 52
 
6.1%
2 44
 
5.2%
4 40
 
4.7%
3 40
 
4.7%
S 40
 
4.7%
1 37
 
4.3%
A 37
 
4.3%
T 36
 
4.2%
5 36
 
4.2%
Other values (23) 286
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 206
24.1%
I 52
 
6.1%
2 44
 
5.2%
4 40
 
4.7%
3 40
 
4.7%
S 40
 
4.7%
1 37
 
4.3%
A 37
 
4.3%
T 36
 
4.2%
5 36
 
4.2%
Other values (23) 286
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 206
24.1%
I 52
 
6.1%
2 44
 
5.2%
4 40
 
4.7%
3 40
 
4.7%
S 40
 
4.7%
1 37
 
4.3%
A 37
 
4.3%
T 36
 
4.2%
5 36
 
4.2%
Other values (23) 286
33.5%

fecha
Date

Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum2023-02-22 00:00:00
Maximum2023-11-14 00:00:00
2024-10-11T18:42:45.126006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:45.252802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:45.409715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.0059172
Min length5

Characters and Unicode

Total characters1184
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerano
2nd rowotoño
3rd rowinvierno
4th rowprimavera
5th rowVerano
ValueCountFrequency (%)
invierno 43
25.4%
verano 42
24.9%
otoño 42
24.9%
primavera 42
24.9%
2024-10-11T18:42:45.740903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 211
17.8%
r 169
14.3%
i 128
10.8%
n 128
10.8%
e 127
10.7%
a 126
10.6%
v 85
7.2%
V 42
 
3.5%
t 42
 
3.5%
ñ 42
 
3.5%
Other values (2) 84
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 211
17.8%
r 169
14.3%
i 128
10.8%
n 128
10.8%
e 127
10.7%
a 126
10.6%
v 85
7.2%
V 42
 
3.5%
t 42
 
3.5%
ñ 42
 
3.5%
Other values (2) 84
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 211
17.8%
r 169
14.3%
i 128
10.8%
n 128
10.8%
e 127
10.7%
a 126
10.6%
v 85
7.2%
V 42
 
3.5%
t 42
 
3.5%
ñ 42
 
3.5%
Other values (2) 84
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 211
17.8%
r 169
14.3%
i 128
10.8%
n 128
10.8%
e 127
10.7%
a 126
10.6%
v 85
7.2%
V 42
 
3.5%
t 42
 
3.5%
ñ 42
 
3.5%
Other values (2) 84
 
7.1%

tem_agua
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)39.9%
Missing31
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean19.796957
Minimum12
Maximum29.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:45.898754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12.555
Q116.075
median19
Q323
95-th percentile26.92
Maximum29.4
Range17.4
Interquartile range (IQR)6.925

Descriptive statistics

Standard deviation4.4879166
Coefficient of variation (CV)0.2266973
Kurtosis-0.96668625
Mean19.796957
Median Absolute Deviation (MAD)3.745
Skewness0.12653042
Sum2731.98
Variance20.141395
MonotonicityNot monotonic
2024-10-11T18:42:46.074587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 19
 
11.2%
22 11
 
6.5%
17 10
 
5.9%
23 9
 
5.3%
19 6
 
3.6%
21 6
 
3.6%
16 6
 
3.6%
20 5
 
3.0%
12 5
 
3.0%
24 4
 
2.4%
Other values (45) 57
33.7%
(Missing) 31
18.3%
ValueCountFrequency (%)
12 5
3.0%
12.2 1
 
0.6%
12.3 1
 
0.6%
12.6 1
 
0.6%
12.9 1
 
0.6%
13.1 1
 
0.6%
13.5 1
 
0.6%
13.7 2
 
1.2%
13.8 2
 
1.2%
14.3 1
 
0.6%
ValueCountFrequency (%)
29.4 1
 
0.6%
28.7 1
 
0.6%
28.5 1
 
0.6%
28 2
1.2%
27.8 1
 
0.6%
27.6 1
 
0.6%
26.8 1
 
0.6%
26.4 1
 
0.6%
26.1 1
 
0.6%
26 3
1.8%

tem_aire
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)15.5%
Missing27
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean18.810563
Minimum10
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:46.246944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q113
median19.5
Q324
95-th percentile28
Maximum30
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.9069223
Coefficient of variation (CV)0.31402155
Kurtosis-1.4602219
Mean18.810563
Median Absolute Deviation (MAD)5.5
Skewness0.080832879
Sum2671.1
Variance34.891732
MonotonicityNot monotonic
2024-10-11T18:42:46.405272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13 18
10.7%
25 15
8.9%
15 15
8.9%
22 13
 
7.7%
24 11
 
6.5%
14 8
 
4.7%
28 7
 
4.1%
23 7
 
4.1%
12 7
 
4.1%
11 7
 
4.1%
Other values (12) 34
20.1%
(Missing) 27
16.0%
ValueCountFrequency (%)
10 6
 
3.6%
11 7
 
4.1%
11.3 2
 
1.2%
11.5 1
 
0.6%
12 7
 
4.1%
13 18
10.7%
14 8
4.7%
15 15
8.9%
16 2
 
1.2%
18 3
 
1.8%
ValueCountFrequency (%)
30 1
 
0.6%
29 3
 
1.8%
28 7
4.1%
27 1
 
0.6%
26 5
 
3.0%
25 15
8.9%
24 11
6.5%
23 7
4.1%
22 13
7.7%
21 5
 
3.0%

od
Real number (ℝ)

MISSING 

Distinct123
Distinct (%)92.5%
Missing36
Missing (%)21.3%
Infinite0
Infinite (%)0.0%
Mean5.9421053
Minimum0.2
Maximum12.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:46.608727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.974
Q14.86
median5.94
Q37.43
95-th percentile9.558
Maximum12.62
Range12.42
Interquartile range (IQR)2.57

Descriptive statistics

Standard deviation2.3289717
Coefficient of variation (CV)0.39194386
Kurtosis0.31604852
Mean5.9421053
Median Absolute Deviation (MAD)1.22
Skewness-0.031959836
Sum790.3
Variance5.4241092
MonotonicityNot monotonic
2024-10-11T18:42:46.780381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 3
 
1.8%
5.85 2
 
1.2%
8.53 2
 
1.2%
5.4 2
 
1.2%
5.02 2
 
1.2%
6.4 2
 
1.2%
5.94 2
 
1.2%
6.33 2
 
1.2%
7.6 2
 
1.2%
4.72 1
 
0.6%
Other values (113) 113
66.9%
(Missing) 36
 
21.3%
ValueCountFrequency (%)
0.2 1
0.6%
0.6 1
0.6%
0.96 1
0.6%
1.1 1
0.6%
1.2 1
0.6%
1.22 1
0.6%
1.95 1
0.6%
1.99 1
0.6%
2.01 1
0.6%
2.2 1
0.6%
ValueCountFrequency (%)
12.62 1
0.6%
11.7 1
0.6%
11.49 1
0.6%
10.88 1
0.6%
10.02 1
0.6%
9.88 1
0.6%
9.78 1
0.6%
9.41 1
0.6%
9.3 1
0.6%
9.05 1
0.6%

ph
Real number (ℝ)

MISSING 

Distinct94
Distinct (%)75.2%
Missing44
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean7.38152
Minimum5.31
Maximum10.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:46.955589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.31
5-th percentile6.492
Q16.91
median7.3
Q37.7
95-th percentile8.7
Maximum10.5
Range5.19
Interquartile range (IQR)0.79

Descriptive statistics

Standard deviation0.77393637
Coefficient of variation (CV)0.10484783
Kurtosis2.5385223
Mean7.38152
Median Absolute Deviation (MAD)0.39
Skewness0.91047017
Sum922.69
Variance0.59897751
MonotonicityNot monotonic
2024-10-11T18:42:47.135904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.3 4
 
2.4%
7.39 3
 
1.8%
7.5 3
 
1.8%
7.48 3
 
1.8%
7.13 3
 
1.8%
6.49 2
 
1.2%
7.04 2
 
1.2%
7.05 2
 
1.2%
7.08 2
 
1.2%
6.91 2
 
1.2%
Other values (84) 99
58.6%
(Missing) 44
26.0%
ValueCountFrequency (%)
5.31 1
0.6%
5.32 1
0.6%
6 1
0.6%
6.32 1
0.6%
6.4 1
0.6%
6.49 2
1.2%
6.5 2
1.2%
6.53 1
0.6%
6.57 1
0.6%
6.58 1
0.6%
ValueCountFrequency (%)
10.5 1
0.6%
9.59 1
0.6%
9.39 1
0.6%
9.38 1
0.6%
9.18 1
0.6%
9.04 1
0.6%
8.7 2
1.2%
8.69 1
0.6%
8.67 1
0.6%
8.55 1
0.6%

olores
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Memory size470.0 B
False
140 
True
 
11
(Missing)
18 
ValueCountFrequency (%)
False 140
82.8%
True 11
 
6.5%
(Missing) 18
 
10.7%
2024-10-11T18:42:47.271998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Memory size470.0 B
False
142 
True
 
9
(Missing)
18 
ValueCountFrequency (%)
False 142
84.0%
True 9
 
5.3%
(Missing) 18
 
10.7%
2024-10-11T18:42:47.381161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Memory size470.0 B
False
146 
True
 
5
(Missing)
18 
ValueCountFrequency (%)
False 146
86.4%
True 5
 
3.0%
(Missing) 18
 
10.7%
2024-10-11T18:42:47.490877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Memory size470.0 B
False
104 
True
47 
(Missing)
18 
ValueCountFrequency (%)
False 104
61.5%
True 47
27.8%
(Missing) 18
 
10.7%
2024-10-11T18:42:47.607699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

colif_fecales_ufc_100ml
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct73
Distinct (%)50.7%
Missing25
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean7380.7007
Minimum1
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:47.755146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.06
Q1400
median1250
Q37000
95-th percentile31400
Maximum150000
Range149999
Interquartile range (IQR)6600

Descriptive statistics

Standard deviation16886.745
Coefficient of variation (CV)2.2879595
Kurtosis38.213199
Mean7380.7007
Median Absolute Deviation (MAD)1245.4
Skewness5.4067897
Sum1062820.9
Variance2.8516214 × 108
MonotonicityNot monotonic
2024-10-11T18:42:47.936319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 8
 
4.7%
4000 8
 
4.7%
2000 7
 
4.1%
15000 5
 
3.0%
600 5
 
3.0%
800 5
 
3.0%
7000 5
 
3.0%
200 5
 
3.0%
300 4
 
2.4%
400 4
 
2.4%
Other values (63) 88
52.1%
(Missing) 25
 
14.8%
ValueCountFrequency (%)
1 2
1.2%
1.1 1
 
0.6%
1.3 1
 
0.6%
2 3
1.8%
3 1
 
0.6%
3.4 1
 
0.6%
4 1
 
0.6%
4.1 1
 
0.6%
4.2 1
 
0.6%
5 1
 
0.6%
ValueCountFrequency (%)
150000 1
0.6%
81000 1
0.6%
62000 1
0.6%
52000 1
0.6%
41000 1
0.6%
40000 1
0.6%
38000 1
0.6%
32000 1
0.6%
28000 1
0.6%
23000 1
0.6%

escher_coli_ufc_100ml
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)57.6%
Missing25
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean5170.7472
Minimum1.5
Maximum170000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:48.104404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile19.15
Q1290
median845
Q33000
95-th percentile18850
Maximum170000
Range169998.5
Interquartile range (IQR)2710

Descriptive statistics

Standard deviation16931.906
Coefficient of variation (CV)3.2745568
Kurtosis65.847028
Mean5170.7472
Median Absolute Deviation (MAD)745
Skewness7.4407045
Sum744587.6
Variance2.8668943 × 108
MonotonicityNot monotonic
2024-10-11T18:42:48.268694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 9
 
5.3%
2000 7
 
4.1%
500 7
 
4.1%
100 6
 
3.6%
900 5
 
3.0%
600 5
 
3.0%
200 5
 
3.0%
800 4
 
2.4%
1100 4
 
2.4%
1000 3
 
1.8%
Other values (73) 89
52.7%
(Missing) 25
 
14.8%
ValueCountFrequency (%)
1.5 1
0.6%
2.9 1
0.6%
3 1
0.6%
3.4 1
0.6%
3.7 1
0.6%
4 1
0.6%
4.1 1
0.6%
19 1
0.6%
20 1
0.6%
21 1
0.6%
ValueCountFrequency (%)
170000 1
0.6%
75000 1
0.6%
65000 1
0.6%
38000 1
0.6%
32000 1
0.6%
20000 1
0.6%
19000 2
1.2%
18000 1
0.6%
16000 1
0.6%
15600 1
0.6%

enteroc_ufc_100ml
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)57.6%
Missing25
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean1499.8444
Minimum1
Maximum18400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:48.450346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q1130
median350
Q31040
95-th percentile7360
Maximum18400
Range18399
Interquartile range (IQR)910

Descriptive statistics

Standard deviation3167.0536
Coefficient of variation (CV)2.1115882
Kurtosis14.319774
Mean1499.8444
Median Absolute Deviation (MAD)300
Skewness3.6394308
Sum215977.59
Variance10030229
MonotonicityNot monotonic
2024-10-11T18:42:48.632179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 8
 
4.7%
200 5
 
3.0%
100 4
 
2.4%
400 4
 
2.4%
800 4
 
2.4%
3000 4
 
2.4%
1500 3
 
1.8%
140 3
 
1.8%
360 3
 
1.8%
110 3
 
1.8%
Other values (73) 103
60.9%
(Missing) 25
 
14.8%
ValueCountFrequency (%)
1 1
0.6%
1.19 1
0.6%
1.5 2
1.2%
2 1
0.6%
2.4 1
0.6%
5 1
0.6%
10 2
1.2%
15 1
0.6%
20 1
0.6%
25 1
0.6%
ValueCountFrequency (%)
18400 1
0.6%
18000 1
0.6%
16000 1
0.6%
14000 1
0.6%
10000 1
0.6%
9300 1
0.6%
9000 1
0.6%
7600 1
0.6%
6000 1
0.6%
5700 1
0.6%

nitrato_mg_l
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)58.0%
Missing26
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean7.1629371
Minimum1
Maximum39.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:48.806051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.15
median5.5
Q39.75
95-th percentile18.43
Maximum39.4
Range38.4
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation5.7550306
Coefficient of variation (CV)0.80344565
Kurtosis6.5725942
Mean7.1629371
Median Absolute Deviation (MAD)2.7
Skewness2.0218039
Sum1024.3
Variance33.120377
MonotonicityNot monotonic
2024-10-11T18:42:48.995231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
7.1%
4.2 5
 
3.0%
2.7 4
 
2.4%
3.7 4
 
2.4%
2.8 4
 
2.4%
2.9 4
 
2.4%
5.3 4
 
2.4%
7.3 3
 
1.8%
4.3 3
 
1.8%
3.9 3
 
1.8%
Other values (73) 97
57.4%
(Missing) 26
 
15.4%
ValueCountFrequency (%)
1 12
7.1%
2.1 1
 
0.6%
2.3 2
 
1.2%
2.4 2
 
1.2%
2.5 2
 
1.2%
2.6 1
 
0.6%
2.7 4
 
2.4%
2.8 4
 
2.4%
2.9 4
 
2.4%
3 1
 
0.6%
ValueCountFrequency (%)
39.4 1
0.6%
23.1 1
0.6%
22.5 1
0.6%
21.5 1
0.6%
20.7 1
0.6%
20.2 1
0.6%
20 1
0.6%
18.6 1
0.6%
16.9 1
0.6%
16.7 2
1.2%

nh4_mg_l
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)57.6%
Missing18
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean1.9426159
Minimum0.025
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:49.155372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.025
5-th percentile0.05
Q10.315
median0.77
Q31.9
95-th percentile8.7
Maximum18
Range17.975
Interquartile range (IQR)1.585

Descriptive statistics

Standard deviation3.0256384
Coefficient of variation (CV)1.5575073
Kurtosis8.692953
Mean1.9426159
Median Absolute Deviation (MAD)0.58
Skewness2.7751442
Sum293.335
Variance9.1544876
MonotonicityNot monotonic
2024-10-11T18:42:49.335970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.025 7
 
4.1%
1.2 4
 
2.4%
1.5 4
 
2.4%
0.44 4
 
2.4%
1.9 4
 
2.4%
0.09 4
 
2.4%
0.13 3
 
1.8%
2.1 3
 
1.8%
0.06 3
 
1.8%
1.4 3
 
1.8%
Other values (77) 112
66.3%
(Missing) 18
 
10.7%
ValueCountFrequency (%)
0.025 7
4.1%
0.05 2
 
1.2%
0.06 3
1.8%
0.07 2
 
1.2%
0.09 4
2.4%
0.1 2
 
1.2%
0.11 1
 
0.6%
0.13 3
1.8%
0.17 1
 
0.6%
0.19 1
 
0.6%
ValueCountFrequency (%)
18 1
0.6%
16 1
0.6%
11 2
1.2%
10 2
1.2%
9.1 1
0.6%
8.9 1
0.6%
8.5 1
0.6%
8.4 1
0.6%
8.2 1
0.6%
8 1
0.6%

p_total_l_mg_l
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)48.0%
Missing19
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean0.50833333
Minimum0.05
Maximum3.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:49.526773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.1345
Q10.21
median0.35
Q30.57
95-th percentile1.455
Maximum3.1
Range3.05
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.50837472
Coefficient of variation (CV)1.0000814
Kurtosis10.086122
Mean0.50833333
Median Absolute Deviation (MAD)0.16
Skewness2.8908505
Sum76.25
Variance0.25844485
MonotonicityNot monotonic
2024-10-11T18:42:49.704466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21 7
 
4.1%
0.2 6
 
3.6%
0.26 5
 
3.0%
0.44 5
 
3.0%
0.23 4
 
2.4%
0.16 4
 
2.4%
0.14 4
 
2.4%
0.22 4
 
2.4%
0.19 4
 
2.4%
0.3 4
 
2.4%
Other values (62) 103
60.9%
(Missing) 19
 
11.2%
ValueCountFrequency (%)
0.05 1
 
0.6%
0.1 3
1.8%
0.11 1
 
0.6%
0.12 2
1.2%
0.13 1
 
0.6%
0.14 4
2.4%
0.15 3
1.8%
0.16 4
2.4%
0.17 3
1.8%
0.18 2
1.2%
ValueCountFrequency (%)
3.1 1
0.6%
3 1
0.6%
2.7 1
0.6%
2.1 1
0.6%
2 1
0.6%
1.7 1
0.6%
1.6 1
0.6%
1.5 1
0.6%
1.4 2
1.2%
1.3 1
0.6%

fosf_ortofos_mg_l
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)31.3%
Missing22
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean0.26088435
Minimum0.05
Maximum1.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:49.883316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.1
Q10.1
median0.2
Q30.34
95-th percentile0.591
Maximum1.6
Range1.55
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.21528867
Coefficient of variation (CV)0.82522646
Kurtosis12.503227
Mean0.26088435
Median Absolute Deviation (MAD)0.1
Skewness2.8562452
Sum38.35
Variance0.046349213
MonotonicityNot monotonic
2024-10-11T18:42:50.072230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.1 36
21.3%
0.2 8
 
4.7%
0.18 5
 
3.0%
0.17 5
 
3.0%
0.15 5
 
3.0%
0.05 4
 
2.4%
0.25 4
 
2.4%
0.4 4
 
2.4%
0.23 4
 
2.4%
0.36 4
 
2.4%
Other values (36) 68
40.2%
(Missing) 22
 
13.0%
ValueCountFrequency (%)
0.05 4
 
2.4%
0.1 36
21.3%
0.11 2
 
1.2%
0.12 4
 
2.4%
0.13 2
 
1.2%
0.14 4
 
2.4%
0.15 5
 
3.0%
0.16 2
 
1.2%
0.17 5
 
3.0%
0.18 5
 
3.0%
ValueCountFrequency (%)
1.6 1
0.6%
1.2 1
0.6%
1.1 1
0.6%
0.65 1
0.6%
0.64 1
0.6%
0.61 1
0.6%
0.6 2
1.2%
0.57 1
0.6%
0.56 2
1.2%
0.55 2
1.2%

dbo_mg_l
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)34.0%
Missing19
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean3.908
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:50.247100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3.1
Q34.475
95-th percentile11
Maximum24
Range23
Interquartile range (IQR)3.475

Descriptive statistics

Standard deviation3.7530036
Coefficient of variation (CV)0.9603387
Kurtosis11.129919
Mean3.908
Median Absolute Deviation (MAD)1.5
Skewness2.9692594
Sum586.2
Variance14.085036
MonotonicityNot monotonic
2024-10-11T18:42:50.420317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 40
23.7%
4.1 6
 
3.6%
3.8 5
 
3.0%
2.5 5
 
3.0%
3.1 5
 
3.0%
2.1 4
 
2.4%
4.3 4
 
2.4%
4 4
 
2.4%
2 4
 
2.4%
3 3
 
1.8%
Other values (41) 70
41.4%
(Missing) 19
 
11.2%
ValueCountFrequency (%)
1 40
23.7%
2 4
 
2.4%
2.1 4
 
2.4%
2.2 2
 
1.2%
2.3 3
 
1.8%
2.5 5
 
3.0%
2.6 3
 
1.8%
2.7 3
 
1.8%
2.8 3
 
1.8%
2.9 1
 
0.6%
ValueCountFrequency (%)
24 1
 
0.6%
23 1
 
0.6%
17 3
1.8%
12 2
1.2%
11 2
1.2%
9 1
 
0.6%
8.4 1
 
0.6%
7.9 1
 
0.6%
7.7 1
 
0.6%
7.6 1
 
0.6%

dqo_mg_l
Text

MISSING 

Distinct34
Distinct (%)22.5%
Missing18
Missing (%)10.7%
Memory size1.4 KiB
2024-10-11T18:42:50.594987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.7350993
Min length2

Characters and Unicode

Total characters413
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)15.2%

Sample

1st row<30
2nd row<30
3rd row<30
4th row<30
5th row<30
ValueCountFrequency (%)
30 95
62.9%
50 8
 
5.3%
40 6
 
4.0%
59 4
 
2.6%
32 3
 
2.0%
38 3
 
2.0%
37 3
 
2.0%
52 2
 
1.3%
80 2
 
1.3%
76 2
 
1.3%
Other values (23) 23
 
15.2%
2024-10-11T18:42:50.922943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
29.1%
3 112
27.1%
< 101
24.5%
5 19
 
4.6%
6 11
 
2.7%
4 10
 
2.4%
7 10
 
2.4%
8 8
 
1.9%
1 7
 
1.7%
2 7
 
1.7%
Other values (2) 8
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 120
29.1%
3 112
27.1%
< 101
24.5%
5 19
 
4.6%
6 11
 
2.7%
4 10
 
2.4%
7 10
 
2.4%
8 8
 
1.9%
1 7
 
1.7%
2 7
 
1.7%
Other values (2) 8
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 120
29.1%
3 112
27.1%
< 101
24.5%
5 19
 
4.6%
6 11
 
2.7%
4 10
 
2.4%
7 10
 
2.4%
8 8
 
1.9%
1 7
 
1.7%
2 7
 
1.7%
Other values (2) 8
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 120
29.1%
3 112
27.1%
< 101
24.5%
5 19
 
4.6%
6 11
 
2.7%
4 10
 
2.4%
7 10
 
2.4%
8 8
 
1.9%
1 7
 
1.7%
2 7
 
1.7%
Other values (2) 8
 
1.9%

turbiedad_ntu
Real number (ℝ)

MISSING 

Distinct55
Distinct (%)36.4%
Missing18
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean35.780132
Minimum2.9
Maximum432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:51.149760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile8.35
Q117
median25
Q345
95-th percentile85
Maximum432
Range429.1
Interquartile range (IQR)28

Descriptive statistics

Standard deviation41.161001
Coefficient of variation (CV)1.150387
Kurtosis57.576053
Mean35.780132
Median Absolute Deviation (MAD)12
Skewness6.4151961
Sum5402.8
Variance1694.228
MonotonicityNot monotonic
2024-10-11T18:42:51.388430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 8
 
4.7%
20 7
 
4.1%
17 7
 
4.1%
12 7
 
4.1%
50 7
 
4.1%
23 6
 
3.6%
21 6
 
3.6%
40 5
 
3.0%
14 5
 
3.0%
24 5
 
3.0%
Other values (45) 88
52.1%
(Missing) 18
 
10.7%
ValueCountFrequency (%)
2.9 1
0.6%
3.1 1
0.6%
3.5 1
0.6%
4 1
0.6%
6 1
0.6%
6.1 1
0.6%
6.2 1
0.6%
8 1
0.6%
8.7 1
0.6%
9.3 1
0.6%
ValueCountFrequency (%)
432 1
 
0.6%
150 1
 
0.6%
140 1
 
0.6%
120 1
 
0.6%
110 1
 
0.6%
95 2
1.2%
90 1
 
0.6%
80 1
 
0.6%
75 2
1.2%
70 3
1.8%

hidr_deriv_petr_ug_l
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing19
Missing (%)11.2%
Memory size1.4 KiB
2024-10-11T18:42:51.497532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters750
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<0.10
2nd row<0.10
3rd row<0.10
4th row<0.10
5th row<0.10
ValueCountFrequency (%)
0.10 150
100.0%
2024-10-11T18:42:51.755600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 300
40.0%
< 150
20.0%
. 150
20.0%
1 150
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 300
40.0%
< 150
20.0%
. 150
20.0%
1 150
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 300
40.0%
< 150
20.0%
. 150
20.0%
1 150
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 300
40.0%
< 150
20.0%
. 150
20.0%
1 150
20.0%

cr_total_mg_l
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)10.6%
Missing18
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean0.089122517
Minimum0
Maximum7
Zeros6
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:51.886306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0025
Q10.0025
median0.0025
Q30.0025
95-th percentile0.00755
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7474548
Coefficient of variation (CV)8.3868233
Kurtosis74.783207
Mean0.089122517
Median Absolute Deviation (MAD)0
Skewness8.67968
Sum13.4575
Variance0.55868868
MonotonicityNot monotonic
2024-10-11T18:42:52.028358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0025 126
74.6%
0 6
 
3.6%
0.005 3
 
1.8%
0.007 3
 
1.8%
0.006 2
 
1.2%
7 1
 
0.6%
0.0051 1
 
0.6%
6 1
 
0.6%
0.033 1
 
0.6%
0.0055 1
 
0.6%
Other values (6) 6
 
3.6%
(Missing) 18
 
10.7%
ValueCountFrequency (%)
0 6
 
3.6%
0.0025 126
74.6%
0.005 3
 
1.8%
0.0051 1
 
0.6%
0.0055 1
 
0.6%
0.006 2
 
1.2%
0.007 3
 
1.8%
0.0075 1
 
0.6%
0.0076 1
 
0.6%
0.0078 1
 
0.6%
ValueCountFrequency (%)
7 1
 
0.6%
6 1
 
0.6%
0.033 1
 
0.6%
0.011 1
 
0.6%
0.009 1
 
0.6%
0.008 1
 
0.6%
0.0078 1
 
0.6%
0.0076 1
 
0.6%
0.0075 1
 
0.6%
0.007 3
1.8%

cd_total_mg_l
Real number (ℝ)

MISSING 

Distinct2
Distinct (%)1.3%
Missing18
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean0.13612583
Minimum0.0005
Maximum0.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:52.155564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.0005
Q10.0005
median0.0005
Q30.5
95-th percentile0.5
Maximum0.5
Range0.4995
Interquartile range (IQR)0.4995

Descriptive statistics

Standard deviation0.22288953
Coefficient of variation (CV)1.6373787
Kurtosis-0.93556532
Mean0.13612583
Median Absolute Deviation (MAD)0
Skewness1.037788
Sum20.555
Variance0.049679741
MonotonicityNot monotonic
2024-10-11T18:42:52.275912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0.0005 110
65.1%
0.5 41
 
24.3%
(Missing) 18
 
10.7%
ValueCountFrequency (%)
0.0005 110
65.1%
0.5 41
 
24.3%
ValueCountFrequency (%)
0.5 41
 
24.3%
0.0005 110
65.1%

clorofila_a_ug_l
Real number (ℝ)

MISSING 

Distinct95
Distinct (%)64.2%
Missing21
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean22.6
Minimum0.05
Maximum740.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:52.438514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.05
Q12.5975
median5
Q318.465
95-th percentile99.3665
Maximum740.93
Range740.88
Interquartile range (IQR)15.8675

Descriptive statistics

Standard deviation67.025536
Coefficient of variation (CV)2.9657317
Kurtosis90.89044
Mean22.6
Median Absolute Deviation (MAD)4.11
Skewness8.7279428
Sum3344.8
Variance4492.4225
MonotonicityNot monotonic
2024-10-11T18:42:52.609605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 27
 
16.0%
0.05 13
 
7.7%
1.31 3
 
1.8%
3.85 3
 
1.8%
2.61 3
 
1.8%
1.81 2
 
1.2%
5.99 2
 
1.2%
2.79 2
 
1.2%
0.35 2
 
1.2%
10.7 2
 
1.2%
Other values (85) 89
52.7%
(Missing) 21
 
12.4%
ValueCountFrequency (%)
0.05 13
7.7%
0.35 2
 
1.2%
0.36 1
 
0.6%
0.42 1
 
0.6%
0.45 2
 
1.2%
0.59 1
 
0.6%
0.67 1
 
0.6%
0.87 2
 
1.2%
0.91 1
 
0.6%
0.94 1
 
0.6%
ValueCountFrequency (%)
740.93 1
0.6%
174.32 1
0.6%
152.11 1
0.6%
133.41 1
0.6%
126.75 1
0.6%
120.95 1
0.6%
101.82 2
1.2%
94.81 1
0.6%
84.8 1
0.6%
82.3 1
0.6%

microcistina_ug_l
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)15.5%
Missing21
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean0.64986486
Minimum0.075
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-11T18:42:52.770242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.075
5-th percentile0.075
Q10.075
median0.075
Q30.16
95-th percentile5
Maximum5
Range4.925
Interquartile range (IQR)0.085

Descriptive statistics

Standard deviation1.4666398
Coefficient of variation (CV)2.2568381
Kurtosis4.7176548
Mean0.64986486
Median Absolute Deviation (MAD)0
Skewness2.5285666
Sum96.18
Variance2.1510323
MonotonicityNot monotonic
2024-10-11T18:42:52.912246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.075 106
62.7%
5 14
 
8.3%
0.16 3
 
1.8%
0.15 3
 
1.8%
0.2 2
 
1.2%
0.41 2
 
1.2%
0.27 2
 
1.2%
0.99 1
 
0.6%
2.61 1
 
0.6%
0.21 1
 
0.6%
Other values (13) 13
 
7.7%
(Missing) 21
 
12.4%
ValueCountFrequency (%)
0.075 106
62.7%
0.15 3
 
1.8%
0.16 3
 
1.8%
0.17 1
 
0.6%
0.18 1
 
0.6%
0.19 1
 
0.6%
0.2 2
 
1.2%
0.21 1
 
0.6%
0.25 1
 
0.6%
0.27 2
 
1.2%
ValueCountFrequency (%)
5 14
8.3%
2.98 1
 
0.6%
2.61 1
 
0.6%
2.42 1
 
0.6%
1.18 1
 
0.6%
0.99 1
 
0.6%
0.9 1
 
0.6%
0.86 1
 
0.6%
0.79 1
 
0.6%
0.69 1
 
0.6%

ica
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)19.0%
Missing32
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean37.20438
Minimum23
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-11T18:42:53.070086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile29
Q135
median37
Q339
95-th percentile45.4
Maximum62
Range39
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.1921019
Coefficient of variation (CV)0.1395562
Kurtosis5.8587029
Mean37.20438
Median Absolute Deviation (MAD)2
Skewness1.3712111
Sum5097
Variance26.957922
MonotonicityNot monotonic
2024-10-11T18:42:53.212034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
37 22
13.0%
35 18
10.7%
40 16
9.5%
38 13
7.7%
39 12
 
7.1%
36 10
 
5.9%
34 8
 
4.7%
32 5
 
3.0%
33 5
 
3.0%
29 4
 
2.4%
Other values (16) 24
14.2%
(Missing) 32
18.9%
ValueCountFrequency (%)
23 1
 
0.6%
25 1
 
0.6%
28 2
 
1.2%
29 4
 
2.4%
30 2
 
1.2%
31 2
 
1.2%
32 5
 
3.0%
33 5
 
3.0%
34 8
4.7%
35 18
10.7%
ValueCountFrequency (%)
62 1
 
0.6%
59 1
 
0.6%
53 1
 
0.6%
49 1
 
0.6%
48 1
 
0.6%
47 2
1.2%
45 3
1.8%
44 1
 
0.6%
43 1
 
0.6%
42 1
 
0.6%

calidad_de_agua
Text

MISSING 

Distinct2
Distinct (%)1.5%
Missing32
Missing (%)18.9%
Memory size1.4 KiB
2024-10-11T18:42:53.386126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length26
Mean length25.19708
Min length15

Characters and Unicode

Total characters3452
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMuy deteriorada
2nd rowExtremadamente deteriorada
3rd rowMuy deteriorada
4th rowExtremadamente deteriorada
5th rowExtremadamente deteriorada
ValueCountFrequency (%)
deteriorada 137
50.0%
extremadamente 127
46.4%
muy 10
 
3.6%
2024-10-11T18:42:54.172256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 655
19.0%
a 528
15.3%
r 401
11.6%
d 401
11.6%
t 391
11.3%
m 254
 
7.4%
137
 
4.0%
i 137
 
4.0%
o 137
 
4.0%
E 127
 
3.7%
Other values (5) 284
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 655
19.0%
a 528
15.3%
r 401
11.6%
d 401
11.6%
t 391
11.3%
m 254
 
7.4%
137
 
4.0%
i 137
 
4.0%
o 137
 
4.0%
E 127
 
3.7%
Other values (5) 284
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 655
19.0%
a 528
15.3%
r 401
11.6%
d 401
11.6%
t 391
11.3%
m 254
 
7.4%
137
 
4.0%
i 137
 
4.0%
o 137
 
4.0%
E 127
 
3.7%
Other values (5) 284
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 655
19.0%
a 528
15.3%
r 401
11.6%
d 401
11.6%
t 391
11.3%
m 254
 
7.4%
137
 
4.0%
i 137
 
4.0%
o 137
 
4.0%
E 127
 
3.7%
Other values (5) 284
8.2%

Interactions

2024-10-11T18:42:39.242326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:40.630695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:43.183011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:45.907615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:48.326401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:50.719812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:53.359003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:57.180179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:59.829837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:03.300250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:06.624106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:10.386439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:15.705323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:20.962174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:27.834510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:32.068265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:34.249140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:36.629163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:39.376268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:40.754690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:43.323310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:46.043561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:48.450727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:50.857544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:53.500208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:57.317145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:59.998815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:03.466010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:06.761805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:10.718000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:15.976156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:21.231655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:28.214549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:32.188732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:34.383759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:36.749503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:39.526061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:40.906182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:43.474509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:46.185276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:48.588379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:51.082778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:53.667439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:57.454808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:00.178217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:03.612190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:06.951475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:11.036548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:16.364586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:22.094082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:28.532597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:32.324469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:34.522968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:36.898655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:39.642472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:41.041464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:43.608871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:46.318584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:48.705606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:51.237449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:53.803327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:57.599620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:00.361611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:03.742537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:07.147603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:11.388837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:16.674262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:22.460468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:28.784688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:32.451087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:34.631646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:37.003802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:39.762599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:41.177519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:43.737811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:46.451931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:48.812617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:51.366049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:53.921382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:57.838721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:00.498517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:03.862195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:07.297646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-10-11T18:42:20.190653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:26.908998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:31.685984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:33.894249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:36.246043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:38.483270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:41.274573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:42.907939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:45.611235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:48.070674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:50.433984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:53.073494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:56.914416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:59.558545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:02.782952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:06.311054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:09.833573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:15.130951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:20.443994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:27.278945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:31.812046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:34.015104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:36.377316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:38.632162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:41.409918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:43.041621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:45.759989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:48.185922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:50.584880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:53.210603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:57.050463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:41:59.700644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:02.965289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:06.456006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:10.103739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:15.415790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:20.693303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:27.570081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:31.932330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:34.127373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:36.494057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T18:42:39.126053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-11T18:42:54.301169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
cd_total_mg_lclorofila_a_ug_lcolif_fecales_ufc_100mlcolorcr_total_mg_ldbo_mg_lenteroc_ufc_100mlescher_coli_ufc_100mlespumasfosf_ortofos_mg_licamat_suspmicrocistina_ug_lnh4_mg_lnitrato_mg_lodoloresp_total_l_mg_lphtem_aguatem_aireturbiedad_ntu
cd_total_mg_l1.000-0.186-0.4950.0910.009-0.276-0.055-0.2210.000-0.009-0.1440.254-0.383-0.1020.1540.0830.117-0.4240.135-0.226-0.3020.111
clorofila_a_ug_l-0.1861.0000.0220.000-0.0380.329-0.2080.0330.000-0.001-0.2900.0000.274-0.137-0.1790.0810.0000.1740.3030.1400.2380.178
colif_fecales_ufc_100ml-0.4950.0221.0000.000-0.0360.3400.3460.7020.3960.217-0.2890.0520.2190.3020.098-0.3430.3330.434-0.1580.1700.122-0.205
color0.0910.0000.0001.0000.3050.2460.0000.0000.1700.4110.4490.3360.0000.5680.2100.2180.4070.2800.0000.0650.1290.000
cr_total_mg_l0.009-0.038-0.0360.3051.0000.092-0.210-0.1350.0000.1640.1280.1280.140-0.1120.105-0.0560.2690.012-0.1310.1810.1910.063
dbo_mg_l-0.2760.3290.3400.2460.0921.0000.1760.3580.2390.374-0.3100.0410.3440.2970.108-0.2590.2500.5750.1360.1670.126-0.082
enteroc_ufc_100ml-0.055-0.2080.3460.000-0.2100.1761.0000.5640.2820.168-0.4410.138-0.2690.4770.261-0.2440.1530.268-0.064-0.288-0.437-0.149
escher_coli_ufc_100ml-0.2210.0330.7020.000-0.1350.3580.5641.0000.4020.344-0.4640.000-0.0070.4990.297-0.3060.2320.449-0.049-0.003-0.144-0.327
espumas0.0000.0000.3960.1700.0000.2390.2820.4021.0000.4160.2990.1330.0000.5110.0000.0000.2940.3010.0000.0000.1810.000
fosf_ortofos_mg_l-0.009-0.0010.2170.4110.1640.3740.1680.3440.4161.000-0.2860.0000.1080.4890.630-0.1380.3230.6900.158-0.285-0.250-0.137
ica-0.144-0.290-0.2890.4490.128-0.310-0.441-0.4640.299-0.2861.0000.3040.056-0.452-0.1810.3330.561-0.2050.133-0.0000.0510.213
mat_susp0.2540.0000.0520.3360.1280.0410.1380.0000.1330.0000.3041.0000.1070.1180.0000.1310.2100.0000.3130.2460.1380.019
microcistina_ug_l-0.3830.2740.2190.0000.1400.344-0.269-0.0070.0000.1080.0560.1071.000-0.175-0.1420.0910.0000.2320.2040.3000.3910.132
nh4_mg_l-0.102-0.1370.3020.568-0.1120.2970.4770.4990.5110.489-0.4520.118-0.1751.0000.377-0.3760.4710.428-0.044-0.270-0.263-0.454
nitrato_mg_l0.154-0.1790.0980.2100.1050.1080.2610.2970.0000.630-0.1810.000-0.1420.3771.000-0.0300.2120.3830.095-0.474-0.457-0.288
od0.0830.081-0.3430.218-0.056-0.259-0.244-0.3060.000-0.1380.3330.1310.091-0.376-0.0301.0000.000-0.1170.377-0.146-0.1560.412
olores0.1170.0000.3330.4070.2690.2500.1530.2320.2940.3230.5610.2100.0000.4710.2120.0001.0000.3370.0000.2520.0860.000
p_total_l_mg_l-0.4240.1740.4340.2800.0120.5750.2680.4490.3010.690-0.2050.0000.2320.4280.383-0.1170.3371.0000.187-0.202-0.147-0.138
ph0.1350.303-0.1580.000-0.1310.136-0.064-0.0490.0000.1580.1330.3130.204-0.0440.0950.3770.0000.1871.000-0.287-0.3380.359
tem_agua-0.2260.1400.1700.0650.1810.167-0.288-0.0030.000-0.285-0.0000.2460.300-0.270-0.474-0.1460.252-0.202-0.2871.0000.823-0.022
tem_aire-0.3020.2380.1220.1290.1910.126-0.437-0.1440.181-0.2500.0510.1380.391-0.263-0.457-0.1560.086-0.147-0.3380.8231.000-0.126
turbiedad_ntu0.1110.178-0.2050.0000.063-0.082-0.149-0.3270.000-0.1370.2130.0190.132-0.454-0.2880.4120.000-0.1380.359-0.022-0.1261.000

Missing values

2024-10-11T18:42:41.625000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-11T18:42:42.128378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-11T18:42:42.591544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sitioscodigofechacampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
0Canal Villanueva y Río LujánTI0012023-02-22Verano26.024.06.597.24FalseFalseFalseTrue600.0100.0130.03.90.0250.260.051.0<3029.0<0.100.00250.00050.870.07553Muy deteriorada
1Canal Villanueva y Río LujánTI0012023-05-10otoño18.012.07.097.22FalseFalseFalseFalse3200.02200.0770.03.90.3000.160.151.0<3045.0<0.100.00250.50002.560.07539Extremadamente deteriorada
2Canal Villanueva y Río LujánTI0012023-08-23invierno16.311.08.537.27FalseFalseFalseTrue240.0200.0300.04.20.1900.570.101.0<3038.0<0.100.00250.00051.020.07548Muy deteriorada
3Canal Villanueva y Río LujánTI0012023-11-14primavera23.025.04.726.57FalseFalseFalseFalse200.0180.0290.03.30.1300.120.101.0<3024.0<0.100.00250.00055.000.07542Extremadamente deteriorada
4Río Lujan y Arroyo CaraguatáTI0062023-02-22Verano26.825.05.946.96FalseFalseFalseTrue1000.0400.01.55.20.2500.160.112.1<3024.0<0.100.00250.000510.610.21039Extremadamente deteriorada
5Río Lujan y Arroyo CaraguatáTI0062023-05-10otoño18.012.07.127.22FalseFalseFalseTrue300.01100.0300.06.70.1300.110.101.0<3060.0<0.100.00250.50000.670.07538Extremadamente deteriorada
6Río Lujan y Arroyo CaraguatáTI0062023-08-23invierno15.011.06.887.13FalseFalseTrueTrue15000.08300.05000.06.30.7200.210.201.03837.0<0.100.00250.00051.810.16034Extremadamente deteriorada
7Río Lujan y Arroyo CaraguatáTI0062023-11-14primavera22.025.02.626.49FalseFalseFalseTrue2000.0400.0800.01.00.2500.140.101.04311.0<0.100.00250.00055.000.07535Extremadamente deteriorada
8Canal Aliviador y Río LujanTI0022023-02-22Verano27.624.06.146.88FalseFalseFalseTrue2000.01000.0100.03.52.2000.410.364.1<3021.0<0.100.00250.000516.872.98035Extremadamente deteriorada
9Canal Aliviador y Río LujanTI0022023-05-10otoño18.012.06.167.33FalseFalseFalseFalse5.03.0200.04.20.8600.210.201.0<3045.0<0.100.00250.50000.050.07537Extremadamente deteriorada
sitioscodigofechacampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
159Playa La BagliardiBS0912023-11-14primavera24.020.04.537.84TrueFalseTrueTrue81000.075000.018400.01.011.000.960.5123.08650.0<0.100.00250.00055.000.07525Extremadamente deteriorada
160Balneario MunicipalBS0942023-02-22Verano20.024.07.538.21FalseFalseFalseTrue700.0400.0330.02.90.510.270.173.75995.0<0.100.00800.000520.525.000<NA>NaN
161Balneario MunicipalBS0942023-05-10otoño17.013.010.029.04FalseFalseFalseFalse1.1700.080.04.20.090.180.151.0<3040.0<0.100.00250.500041.650.07535Extremadamente deteriorada
162Balneario MunicipalBS0942023-11-14primavera20.020.09.788.49FalseFalseFalseFalse1000.0800.0850.01.00.330.380.102.27734.0<0.100.00250.00055.000.07541Extremadamente deteriorada
163Playa La BagliardiBS0912023-08-23invierno12.210.06.338.70FalseFalseFalseFalse600.0600.0360.010.53.201.701.104.07690.0<0.100.00250.00058.220.07537Extremadamente deteriorada
164Balneario MunicipalBS0942023-08-23invierno12.013.09.418.67FalseFalseFalseFalse300.0300.0150.06.60.560.560.552.36675.0<0.100.00250.000519.250.07536Extremadamente deteriorada
165Playa La BalandraBS0932023-02-22Verano20.023.05.807.47FalseFalseFalseTrue1200.0900.0720.03.20.570.790.533.45817.0<0.100.00250.00050.055.000<NA>NaN
166Playa La BalandraBS0932023-05-10otoño18.013.05.938.35FalseFalseFalseFalse500.0500.0140.03.70.090.170.122.1<3032.0<0.100.00250.500054.870.07536Extremadamente deteriorada
167Playa La BalandraBS0932023-11-14primavera20.014.08.228.30FalseFalseFalseTrue800.0800.0360.04.10.390.210.101.048110.0<0.100.00000.000537.400.07535Extremadamente deteriorada
168Playa La BalandraBS0932023-08-23invierno12.010.08.968.09FalseFalseFalseFalse800.0500.0230.05.70.423.000.552.56750.0<0.100.00250.000545.070.79038Extremadamente deteriorada